使用Pandas在Python中复制Excel的IndexMatch

时间:2017-06-20 22:08:26

标签: excel python-3.x pandas

我有一个excel电子表格,我经常更新(每天2-3次)。此更新需要运行索引匹配以从另一个电子表格中的表中提取值,并将它们写入第一个中的列。这些值会覆盖旧值,而不会创建新列。

我想使用pandas自动执行此过程(和xlwings将数据写入电子表格,但我对该部分没有任何问题)。第一步是用pandas复制excel的INDEXMATCH()。总的来说,函数应该:

  • 获取要作为索引的列的字符串标题,要写入的列以及包含用于匹配读取和放大的值的列的参数。写专栏

  • 迭代写入列;在每次迭代中,在读取列中搜索其对应的匹配列值与写入列的匹配列值匹配的值

  • 如果没有匹配值,请写入NaN或' #N / A'到数据帧(区分0和不匹配很重要)

我希望在pandas中有一个原生的vlookup / indexmatch功能,但我唯一能找到的就是加入或合并数据帧,这不是我想要做的事情 - 我想要覆盖个人数据框中的值,并以任意索引顺序执行。

我设法让它使用特定于脚本的功能非常难看,但我认为尝试将该功能用于其他用途会很有用。经过一些清理和重写后,我得到了以下内容:

##Index Match in Python with pandas
#Remember that dataframes start at 0, excel starts at 1
#This only works if both DFs have the same indices (integers, strings, whatever)
import numpy as np
import pandas as pd

#sample dataframes
d = {'Match Column' : [0.,1.,2.,3.,4.,7.,'string'],
     'Read Column' : ['zero','one','two','three','four','seven','string']}

dfRead = pd.DataFrame(d)

d2 = {'Match Column' : [0.,1.,2.,3.,4.,5.,6.,7.,'8'],
      'Write Column' : [0,0,0,0,0,0,0,0,'0']}

dfWrite = pd.DataFrame(d2)

#test arguments
ReadColumn = 'Read Column'
WriteColumn = 'Write Column'
ReadMatchColumn = 'Match Column'
WriteMatchColumn = 'Match Column'

def indexmatch(dfRead, dfWrite, ReadColumn, WriteColumn, ReadMatchColumn, WriteMatchColumn, skiprows=0):
#convert the string inputs to a column number for each dataframe
    RCNum = np.where(dfRead.columns == ReadColumn)[0][0]
    WCNum = np.where(dfWrite.columns == WriteColumn)[0][0]
    RMCNum = np.where(dfRead.columns == ReadMatchColumn)[0][0]
    WMCNum = np.where(dfWrite.columns == WriteMatchColumn)[0][0]

    for i in range(skiprows,len(dfWrite.index),1):
        match = dfWrite.loc[dfWrite.index[i]][WMCNum] #the value we're using to match the columns    
        try:
            matchind = dfRead.index[np.where(dfRead[ReadMatchColumn] == match)[0][0]]
            value = dfRead.fillna('#N/A').loc[matchind][RCNum] #replaces DF NaN values with excel's #N/A, optional method
            dfWrite.set_value(dfWrite.index[i],WriteColumn,value)
        except KeyError:
            dfWrite.set_value(dfWrite.index[i],WriteColumn,np.nan) #if there is no match, write NaN to the 'cell'
        except IndexError:
            dfWrite.set_value(dfWrite.index[i],WriteColumn,np.nan)

这样做有效,但它并不漂亮,并且它并不能说明何时将列与另一个数据帧的索引匹配(例如,将数据帧与数据透视表匹配)数据帧)。

是否有更强大,更简洁的方法呢?

根据要求,预期的输入和输出:

In [2]: dfRead
Out[2]: 
  Match Column Read Column
0            0        zero
1            1         one
2            2         two
3            3       three
4            4        four
5            7       seven
6       string      string

In [3]: dfWrite
Out[3]: 
  Match Column Write Column
0            0            0
1            1            0
2            2            0
3            3            0
4            4            0
5            5            0
6            6            0
7            7            0
8            8            0

In [4]: indexmatch(dfRead, dfWrite, 'Read Column', 'Write Column', 'Match Column', 'Match Column')
In [5]: dfWrite
Out[7]: 
  Match Column Write Column
0            0         zero
1            1          one
2            2          two
3            3        three
4            4         four
5            5          NaN
6            6          NaN
7            7        seven
8            8          NaN

2 个答案:

答案 0 :(得分:1)

pd.Series.map将把一个系列视为一个参数,将它视为输入带有索引作为键的字典。

在这里应用,看起来像

dfWrite['Write Column'] = dfWrite['Match Column'].map(dfRead.set_index('Match Column')['Read Column'])

dfWrite
Out[409]: 
  Match Column Write Column
0            0         zero
1            1          one
2            2          two
3            3        three
4            4         four
5            5          NaN
6            6          NaN
7            7        seven
8            8          NaN

给予相同的输出

indexmatch(dfRead, dfWrite, 'Read Column', 'Write Column', 'Match Column', 'Match Column')

dfWrite
Out[413]: 
  Match Column Write Column
0            0         zero
1            1          one
2            2          two
3            3        three
4            4         four
5            5          NaN
6            6          NaN
7            7        seven
8            8          NaN

要匹配dfRead的索引,请跳过.set_index(...)步骤。要匹配dfWrite的索引,请将dfWrite['Match Column'].map替换为dfWrite.index.to_series().map

答案 1 :(得分:0)

您也可以使用merge功能:

dfWrite = pd.merge(left=dfWrite.ix[:,['Match Column']], right=dfRead, on='Match Column', how='left')

dfWrite.rename(columns={'Read Column':'Write Column'}, inplace=True)